Apparatus and methods for candidate tracking

ABSTRACT

An apparatus and method for candidate tracking. The apparatus includes a processor that is configured to track a candidate through the recruiting process such that a recruiter may be compensated for their recruiting efforts. The apparatus includes receiving data sets from the recruiter and the employer, receiving a transfer request from either user, authenticating the users, determining if the data sets are the same, and processing the transfer request.

FIELD OF THE INVENTION

The present invention generally relates to the field of human resourcetechnology. In particular, the present invention is directed toapparatuses and methods for candidate tracking.

BACKGROUND

Assorters often find and screen prospective candidates for an employer.Usually, Assorters receive actions including forms of compensation froman endpoint in exchange for finding candidates. There is a need for anapparatus and method to securely track candidates through a recruitmentprocess and facilitate actions between an assorter and an endpoint.

SUMMARY OF THE DISCLOSURE

In an aspect an apparatus for candidate tracking includes: at least aprocessor; and a memory communicatively connected to the at least aprocessor and including instructions configuring the at least aprocessor to: receive assorter-linked data set associated with a firstcandidate; receive endpoint-linked data set associated with a secondcandidate; receive a transfer request, wherein the transfer requestcomprises an action datum; determine a degree of match between theassorter-linked data set and the endpoint-linked data set; identify thesecond candidate as the first candidate as a function of the degree ofmatch; and process the transfer request, wherein processing the transferrequest comprises authenticating the recruiter and the employer.

In another aspect a method for candidate tracking includes receivingassorter-linked data set associated with a first candidate; receivingendpoint-linked data set associated with a second candidate; receiving atransfer request, wherein the transfer request comprises an actiondatum; determining a degree of match between the assorter-linked dataset and the endpoint-linked data set; identifying the second candidateas the first candidate; and processing the transfer request, whereinprocessing the transfer request comprises authenticating the recruiterand the employer.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of anapparatus for candidate tracking;

FIG. 2 is a block diagram illustrating an exemplary embodiment of animmutable sequential listing;

FIG. 3 is a block diagram illustrating an exemplary embodiment of acryptographic accumulator;

FIG. 4 is a block diagram of exemplary machine-learning processes;

FIG. 5 illustrates an exemplary neural network;

FIG. 6 is a block diagram of an exemplary node;

FIG. 7 is a flow diagram illustrating an exemplary embodiment of amethod for candidate tracking;

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatuses and methods for candidate tracking. In an embodiment, acandidate may be tracked by an employer and a recruiter in two datasets. Apparatus and method may compare the two data sets forsimilarities.

Aspects of the present disclosure can be used to compensate a recruiterfor finding and recruiting a candidate. Aspects of the presentdisclosure may prevent employers from acting in bad faith. Exemplaryembodiments illustrating aspects of the present disclosure are describedbelow in the context of several specific examples.

In an embodiment, methods and apparatus described herein may performimplement one or more aspects of a cryptographic system. In oneembodiment, a cryptographic system is a system that converts data from afirst form, known as “plaintext,” which is intelligible when viewed inits intended format, into a second form, known as “cyphertext,” which isnot intelligible when viewed in the same way. Cyphertext may beunintelligible in any format unless first converted back to plaintext.In one embodiment, a process of converting plaintext into cyphertext isknown as “encryption.” Encryption may involve the use of a datum, knownas an “encryption key,” to alter plaintext. Cryptographic system mayalso convert cyphertext back into plaintext, which is a process known as“decryption.” Decryption process may involve the use of a datum, knownas a “decryption key,” to return the cyphertext to its originalplaintext form. In embodiments of cryptographic systems that are“symmetric,” decryption key is essentially the same as encryption key:possession of either key makes it possible to deduce the other keyquickly without further secret knowledge. Encryption and decryption keysin symmetric cryptographic systems may be kept secret and shared onlywith persons or entities that the user of the cryptographic systemwishes to be able to decrypt the cyphertext. One example of a symmetriccryptographic system is the Advanced Encryption Standard (“AES”), whicharranges plaintext into matrices and then modifies the matrices throughrepeated permutations and arithmetic operations with an encryption key.

In embodiments of cryptographic systems that are “asymmetric,” eitherencryption or decryption key cannot be readily deduced withoutadditional secret knowledge, even given the possession of acorresponding decryption or encryption key, respectively; a commonexample is a “public key cryptographic system,” in which possession ofthe encryption key does not make it practically feasible to deduce thedecryption key, so that the encryption key may safely be made availableto the public. An example of a public key cryptographic system is RSA,in which an encryption key involves the use of numbers that are productsof very large prime numbers, but a decryption key involves the use ofthose very large prime numbers, such that deducing the decryption keyfrom the encryption key requires the practically infeasible task ofcomputing the prime factors of a number which is the product of two verylarge prime numbers. A further example of an asymmetric cryptographicsystem may include a discrete-logarithm based system based upon therelative ease of computing exponents mod a large integer, and thecomputational infeasibility of determining the discrete logarithm ofresulting numbers absent previous knowledge of the exponentiations; anexample of such a system may include Diffie-Hellman key exchange and/orpublic key encryption. Another example is elliptic curve cryptography,which relies on the fact that given two points P and Q on an ellipticcurve over a finite field, a definition of the inverse of a point −A asthe point with negative y-coordinates, and a definition for additionwhere A+B=−R, the point where a line connecting point A and point Bintersects the elliptic curve, where “0,” the identity, is a point atinfinity in a projective plane containing the elliptic curve, finding anumber k such that adding P to itself k times results in Q iscomputationally impractical, given correctly selected elliptic curve,finite field, and P and Q. A further example of asymmetricalcryptography may include lattice-based cryptography, which relies on thefact that various properties of sets of integer combination of basisvectors are hard to compute, such as finding the one combination ofbasis vectors that results in the smallest Euclidean distance.Embodiments of cryptography, whether symmetrical or asymmetrical, mayinclude quantum-secure cryptography, defined for the purposes of thisdisclosure as cryptography that remains secure against adversariespossessing quantum computers; some forms of lattice-based cryptography,for instance, may be quantum-secure.

In some embodiments, apparatus and methods described herein producecryptographic hashes, also referred to by the equivalent shorthand term“hashes.” A cryptographic hash, as used herein, is a mathematicalrepresentation of a lot of data, such as files or blocks in a blockchain as described in further detail below; the mathematicalrepresentation is produced by a lossy “one-way” algorithm known as a“hashing algorithm.” Hashing algorithm may be a repeatable process; thatis, identical lots of data may produce identical hashes each time theyare subjected to a particular hashing algorithm. Because hashingalgorithm is a one-way function, it may be impossible to reconstruct alot of data from a hash produced from the lot of data using the hashingalgorithm. In the case of some hashing algorithms, reconstructing thefull lot of data from the corresponding hash using a partial set of datafrom the full lot of data may be possible only by repeatedly guessing atthe remaining data and repeating the hashing algorithm; it is thuscomputationally difficult if not infeasible for a single computer toproduce the lot of data, as the statistical likelihood of correctlyguessing the missing data may be extremely low. However, the statisticallikelihood of a computer of a set of computers simultaneously attemptingto guess the missing data within a useful timeframe may be higher,permitting mining protocols as described in further detail below.

In an embodiment, hashing algorithm may demonstrate an “avalancheeffect,” whereby even extremely small changes to lot of data producedrastically different hashes. This may thwart attempts to avoid thecomputational work necessary to recreate a hash by simply inserting afraudulent datum in data lot, enabling the use of hashing algorithms for“tamper-proofing” data such as data contained in an immutable ledger asdescribed in further detail below. This avalanche or “cascade” effectmay be evinced by various hashing processes; persons skilled in the art,upon reading the entirety of this disclosure, will be aware of varioussuitable hashing algorithms for purposes described herein. Verificationof a hash corresponding to a lot of data may be performed by running thelot of data through a hashing algorithm used to produce the hash. Suchverification may be computationally expensive, albeit feasible,potentially adding up to significant processing delays where repeatedhashing, or hashing of large quantities of data, is required, forinstance as described in further detail below. Examples of hashingprograms include, without limitation, SHA256, a NIST standard; furthercurrent and past hashing algorithms include Winternitz hashingalgorithms, various generations of Secure Hash Algorithm (including“SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as“MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny(e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), MessageAuthentication Code (“MAC”)-family hash functions such as PMAC, OMAC,VMAC, HMAC, and UMAC, Polyl305-AES, Elliptic Curve Only Hash (“ECOH”)and similar hash functions, Fast-Syndrome-based (FSB) hash functions,GOST hash functions, the Grøstl hash function, the HAS-160 hashfunction, the JH hash function, the RadioGatún hash function, the Skeinhash function, the Streebog hash function, the SWIFFT hash function, theTiger hash function, the Whirlpool hash function, or any hash functionthat satisfies, at the time of implementation, the requirements that acryptographic hash be deterministic, infeasible to reverse-hash,infeasible to find collisions, and have the property that small changesto an original message to be hashed will change the resulting hash soextensively that the original hash and the new hash appear uncorrelatedto each other. A degree of security of a hash function in practice maydepend both on the hash function itself and on characteristics of themessage and/or digest used in the hash function. For example, where amessage is random, for a hash function that fulfillscollision-resistance requirements, a brute-force or “birthday attack”may to detect collision may be on the order of O(2^(n/2)) for n outputbits; thus, it may take on the order of 2²⁵⁶ operations to locate acollision in a 512 bit output “Dictionary” attacks on hashes likely tohave been generated from a non-random original text can have a lowercomputational complexity, because the space of entries they are guessingis far smaller than the space containing all random permutations ofbits. However, the space of possible messages may be augmented byincreasing the length or potential length of a possible message, or byimplementing a protocol whereby one or more randomly selected strings orsets of data are added to the message, rendering a dictionary attacksignificantly less effective.

Embodiments of apparatus and methods described herein may generate,evaluate, and/or utilize digital signatures. A “digital signature,” asused herein, includes a secure proof of possession of a secret by asigning device, as performed on provided element of data, known as a“message.” A message may include an encrypted mathematicalrepresentation of a file or other set of data using the private key of apublic key cryptographic system. Secure proof may include any form ofsecure proof as described in further detail below, including withoutlimitation encryption using a private key of a public key cryptographicsystem as described above. Signature may be verified using averification datum suitable for verification of a secure proof; forinstance, where secure proof is enacted by encrypting message using aprivate key of a public key cryptographic system, verification mayinclude decrypting the encrypted message using the corresponding publickey and comparing the decrypted representation to a purported match thatwas not encrypted; if the signature protocol is well-designed andimplemented correctly, this means the ability to create the digitalsignature is equivalent to possession of the private decryption keyand/or device-specific secret. Likewise, if a message making up amathematical representation of file is well-designed and implementedcorrectly, any alteration of the file may result in a mismatch with thedigital signature; the mathematical representation may be produced usingan alteration-sensitive, reliably reproducible algorithm, such as ahashing algorithm as described above. A mathematical representation towhich the signature may be compared may be included with signature, forverification purposes; in other embodiments, the algorithm used toproduce the mathematical representation may be publicly available,permitting the easy reproduction of the mathematical representationcorresponding to any file.

In some embodiments, digital signatures may be combined with orincorporated in digital certificates. In one embodiment, a digitalcertificate is a file that conveys information and links the conveyedinformation to a “certificate authority” that is the issuer of a publickey in a public key cryptographic system. Certificate authority in someembodiments contains data conveying the certificate authority'sauthorization for the recipient to perform a task. The authorization maybe the authorization to access a given datum. The authorization may bethe authorization to access a given process. In some embodiments, thecertificate may identify the certificate authority. The digitalcertificate may include a digital signature.

In some embodiments, a third party such as a certificate authority (CA)is available to verify that the possessor of the private key is aparticular entity; thus, if the certificate authority may be trusted,and the private key has not been stolen, the ability of an entity toproduce a digital signature confirms the identity of the entity andlinks the file to the entity in a verifiable way. Digital signature maybe incorporated in a digital certificate, which is a documentauthenticating the entity possessing the private key by authority of theissuing certificate authority and signed with a digital signaturecreated with that private key and a mathematical representation of theremainder of the certificate. In other embodiments, digital signature isverified by comparing the digital signature to one known to have beencreated by the entity that purportedly signed the digital signature; forinstance, if the public key that decrypts the known signature alsodecrypts the digital signature, the digital signature may be consideredverified. Digital signature may also be used to verify that the file hasnot been altered since the formation of the digital signature.

Referring now to FIG. 1 , an exemplary embodiment of an apparatus 100for candidate tracking is shown. System includes a processor 104.Processor 104 may include any processor 104 as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Processor 104 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Processor 104 may include a single computingdevice operating independently, or may include two or more computingdevice operating in concert, in parallel, sequentially or the like; twoor more computing devices may be included together in a single computingdevice or in two or more computing devices. Processor 104 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting processor 104 to one or more of a varietyof networks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. Processor 104 may include butis not limited to, for example, a computing device or cluster ofcomputing devices in a first location and a second computing device orcluster of computing devices in a second location. Processor 104 mayinclude one or more computing devices dedicated to data storage,security, distribution of traffic for load balancing, and the like.Processor 104 may distribute one or more computing tasks as describedbelow across a plurality of computing devices of processor 104, whichmay operate in parallel, in series, redundantly, or in any other mannerused for distribution of tasks or memory between computing devices.Processor 104 may be implemented using a “shared nothing” architecturein which data is cached at the worker, in an embodiment, this may enablescalability of apparatus 100 and/or processor 104.

With continued reference to FIG. 1 , processor 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, processor 104 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , processor 104 is configured toreceive assorter-linked data set 124 associated with a first candidate144. As used in this disclosure, an “assorter” is an entity that assistsin acquiring and placing candidates. An assorter may be a job recruiter.Recruiters often work with employers to decide on potential candidates.An employer may be an example of an endpoint. As used in thisdisclosure, an “endpoint” is an entity that a candidate works for. In anembodiment, an endpoint 112 may be a person, firm, company, or otherentity that hires and pays a person to do a job. As used in thisdisclosure, a “candidate” is a prospective user that is looking to beemployed by an endpoint 112. A candidate may be sought out by anassorter 108 for an endpoint 112. An assorter 108 may seek out acandidate using a resume that may be a video or written resume. As usedin this disclosure, a “assorter-linked data set” is a set of data arecruiter has about a candidate. Assorter-linked data set 124 mayinclude information the assorter 108 has gathered on the candidatethrough interviews. A assorter-linked data set 124 may include a videoresume of a candidate. As used in this disclosure, a “resume” is an itemof media that includes content representative or communicative of ajobseeker. A resume may include personally identifiable informationabout a job seeker such as the jobseeker's name, address, phone number,etc. A resume may include information about a jobseeker's employmenthistory, education, skills, etc. A resume may include a video resume. A“video resume”, as used in this disclosure, is a resume that representsa jobseeker directly, in person, by video. A video resume may be anyvideo in visual and/or audio form to provide a recording promoting ajobseeker. Application data may include visual content such thatuser-specific historical record may include visual content. Visualcontent may be in the form of written content or a video. Video mayinclude visual content that is non-verbal. For example and withoutlimitation, video may include change in intonation and/or stress inspeaker's voice, expression of emotion, interjection, and the like. Anassorter-linked data set 124 may be posted on an immutable sequentiallisting.

Immutable sequential listing 200 may comprise of blocks containingentries of data. For example, an assorter-linked data set 124 may haveblocks representing the candidate's (also referred to as “applicant”)resume, the progress of the candidate through the recruitment process,or the like. In an embodiment, a recruitment process may include blocksrepresenting the candidate passing different interviews (i.e. therecruiter screening, the hiring manager interview, etc.), test resultsfor personality tests, skills assessment tests, job knowledge tests, andthe like. Entries of data may also comprise of records of transactions,such as Bitcoin transactions, or other payment transactions.Additionally, entries of data may comprise of files, such as JPEGs,documents, spreadsheets, videos, pictures, etc. Blocks of the immutablesequential listing 200 may be hashed and encoded into a Merkle tree. Inan embodiment, each block includes the cryptographic hash of the priorblock, linking the blocks and creating a chain. The top of the Merkletree may comprise a Merkle root that may comprise a cryptographicaccumulator 300. The immutable sequential listing 200 includes acryptographic accumulator 300, discussed in further detail in FIG. 3 . A“cryptographic accumulator,” as used in this disclosure, is a datastructure created by relating a commitment, which may be smaller amountof data that may be referred to as an “accumulator” and/or “root,” to aset of elements, such as lots of data and/or collection of data,together with short membership and/or nonmembership proofs for anyelement in the set. In an embodiment, these proofs may be publiclyverifiable against the commitment. An accumulator may be said to be“dynamic” if the commitment and membership proofs can be updatedefficiently as elements are added or removed from the set, at unit costindependent of the number of accumulated elements; an accumulator forwhich this is not the case may be referred to as “static.” A membershipproof may be referred to as a as a “witness” whereby an element existingin the larger amount of data can be shown to be included in the root,while an element not existing in the larger amount of data can be shownnot to be included in the root, where “inclusion” indicates that theincluded element was a part of the process of generating the root, andtherefore was included in the original larger data set.

Continuing to reference FIG. 1 , processor 104 is also configured toreceived endpoint-linked data set 128 associated with a second candidate148. As used in this disclosure, a “endpoint-linked data set” is a setof data an employer has about a candidate. In an embodiment,endpoint-linked data set 128 may contain information similar to theassorter-linked data set 124 such as a resume of the candidate, andrecords of the candidate throughout the recruitment process. Recruitmentprocess records may include information on tests a candidate has taken,interviews a candidate has taken, and the like. Endpoint-linked data set128 may be stored on an immutable sequential listing and managed by anendpoint 112. Endpoint 112 may digitally sign the endpoint-linked dataset 128, whereas an assorter 108 may digitally sign the assorter-linkeddata set 124. In an embodiment, endpoint 112 may hold the private key tothe endpoint-linked data set 128. In another embodiment, assorter 108may hold the private key to the assorter-linked data set 124.

Still referring to FIG. 1 , processor 104 is configured to receive atransfer request 116. As used herein, a “transfer request” is a requestfrom an assorter 108 to an endpoint 112 requesting an action to becompleted. An “action” as used herein, is a deed agreed upon by bothparties. In an embodiment, an action may include a transaction, such asan exchange of money for actions completed by the assorter 108, such asfinding a job applicant. Transfer request 116 includes an action datum132, wherein action datum 132 may include actions such as transferringpayment, transferring resources, and the like. In an embodiment, actiondatum 132 may also include information on the candidate in discussionbetween the endpoint 112 and the assorter 108. In an embodiment, anaction datum 132 may include compensation data. Compensation data mayinclude payments agreed upon between the assorter 108 and the endpoint112, a fee concurred by an endpoint 112 for recruitment services, andthe like. Compensation data may be generated using a machine-learningmodule 120, discussed in further detail in FIG. 4 . Machine-learningmodule 120 may use a classifier to generate compensation data. A“classifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. In an embodiment, processer may use a dataclassifier. As used in this disclosure, a “data classifier” is anidentifying indicia relating to the compensation data, for example,education level, traits, skills, jobs. In an embodiment, processor 104may classify compensation data into categories associated with jobs,education level, types of certifications, experience related todifferent degrees, and the like. A classifier may be configured tooutput at least a datum that labels or otherwise identifies a set ofdata that are clustered together, found to be close under a distancemetric as described below, or the like. Processor 104 and/or anotherdevice may generate a classifier using a classification algorithm,defined as a processes whereby a processor 104 derives a classifier fromtraining data. In an embodiment, training data may be made up of aplurality of training examples that each include examples of data to beinputted into the machine-learning module 120, such as experienceassociated with types of jobs, jobs associated with different degrees(like a mechanical engineering job for a degree in mechanicalengineering), etc., and examples of data to be output therefrom, such asdata sets that include job history associated with different degrees,experience history associated with a job, etc. Training data may beimplemented in any manner discussed below. Training data may be obtainedfrom and/or in the form of previous data set categorization.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers.

Still referring to FIG. 1 , processor 104 may be configured to generatea classifier using a Naïve Bayes classification algorithm. Naïve Bayesclassification algorithm generates classifiers by assigning class labelsto problem instances, represented as vectors of element values. Classlabels are drawn from a finite set. Naïve Bayes classification algorithmmay include generating a family of algorithms that assume that the valueof a particular element is independent of the value of any otherelement, given a class variable. Naïve Bayes classification algorithmmay be based on Bayes Theorem expressed as P(A/B)=P(B/A) P(A)÷P(B),where P(AB) is the probability of hypothesis A given data B also knownas posterior probability; P(B/A) is the probability of data B given thatthe hypothesis A was true; P(A) is the probability of hypothesis A beingtrue regardless of data also known as prior probability of A; and P(B)is the probability of the data regardless of the hypothesis. A naïveBayes algorithm may be generated by first transforming training datainto a frequency table. Processor 104 may then calculate a likelihoodtable by calculating probabilities of different data entries andclassification labels. Processor 104 may utilize a naïve Bayes equationto calculate a posterior probability for each class. A class containingthe highest posterior probability is the outcome of prediction. NaïveBayes classification algorithm may include a gaussian model that followsa normal distribution. Naïve Bayes classification algorithm may includea multinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , processor 104 may be configured togenerate classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent

Still referring to FIG. 1 , processor 104 is configured to determine adegree of match 136 between the assorter-linked data set 124 and theendpoint-linked data set 128. In an embodiment, after a transfer request116 is received by a processer, either by the endpoint 112 or theassorter 108, along with the assorter-linked and endpoint-linked datasets, processor 104 determined a degree of match 136 between the twodata sets. As used herein, a “degree of match” is a measure ofsimilarity between a assorter-linked data set and a endpoint-linked dataset. Degree of match 136 may be used to identify if the second candidate148 of the endpoint-linked data set 128 is the same as the firstcandidate 144 of the assorter-linked data set 124. In an embodiment,determining the degree of match 136 between the two data sets is toensure that the assorter 108 and the endpoint 112 are referring to thesame candidate. Processor 104 may want to determine that assorter 108and endpoint 112 are referring to the same candidate as transfer request116 may include an action datum 132 that provides compensation to anassorter 108 for finding a prospective candidate that is ultimatelyhired by the endpoint 112. Determination of a degree of match 136between two data sets may include identifying and matching a candidatefrom a video resume. In an embodiment, processor 104 may be configuredto compare the video resume in the assorter-linked data set 124 with thevideo resume in the endpoint-linked data set 128. An initial pass may beused by processor 104 to sort elements of video resumes into categories,and a subsequent pass may involve detailed comparison ofcategory-matched video elements from at least two video resumes to oneanother. For example, the initial pass may include classifying theresumes based on image component, audio component, attributes, or atleast identifying subject indica. For example, an image component mayinclude an image of the subject. As used in this disclosure, an “imagecomponent” may be a visual representation of information, such as aplurality of temporally sequential frames and/or pictures. As used inthis disclosure, an “audio component” is a representation of audio, forexample a sound, a speech, and the like. Attributes may includecandidate's skills, competencies, credentials, talents, and the like. Insome cases, attributes may be explicitly conveyed within video resume.Alternatively, or additionally, in some cases, attributes may beconveyed implicitly with video resume. For example, identifying indicacould include name of candidate, account number, social security number,telephone number, address, and the like. In some embodiments, processor104 may utilize a candidate classifier, which may include any classifierused throughout this disclosure, to run an initial pass over the videoelements of video resumes, break down and categorizes such elementsbefore comparing it to another video resume. As used in this disclosure,a “candidate classifier” is a classifier that classifies assorter-linkeddata set 124 and/or data contained therein, which may include videoresume, to endpoint-linked data set 128 and/or data contained therein,which may also include a video resume (or vise versa). In some cases,candidate classifier may include a trained machine-learning model, whichis trained using candidate training data. As used in this disclosure,“candidate training data” is a training data that correlates one or moreof candidates, candidate-specific data, and candidate attributes to oneor more job descriptions, description-specific data, and job descriptiondata. As used in this disclosure, a “job description datum” is anelement of information associated with a job description. Video resumemay be representative of such job descriptive data. For example, in theinitial pass, video resumes may be categorized based on candidate'sattributes such as credentials. As used in this disclosure,“credentials” are any piece of information that indicates anindividual's qualification to perform a certain task or job. Videoresumes may be grouped based on level of experience, educationalhistory, certifications, and the like.

Still referring to FIG. 1 , after initial pass, during the subsequentpass, video resumes may be compared against one another and ranked basedon similarity before an overall comparison result is computed. After theinitial and subsequent pass have been performed, processor 104 mayutilize the data gathered from the candidate classifier to calculate anoverall comparison score of the video resumes. Comparison between videoresumes may be one of many examples of which classification can occur.In some cases, comparison result may contain a comparison score thatrepresents a degree of match 136 between video resumes. Comparison scoremay be determined by dynamic time warping (DTW) based on a similaritymatrix. Dynamic time warping may include algorithms for measuringsimilarity between two sequences, which may vary in time or speed. Forinstance, similarities in walking patterns may be detected, even if inone video the person was walking slowly and if in another he or she werewalking more quickly, or even if there were accelerations anddeceleration during one observation. DTW has been applied to video,audio, and graphics—indeed, any data that can be turned into a linearrepresentation can be analyzed with DTW. In some cases, DTW may allowprocessor 104 to find an optimal match between two given sequences(e.g., time series) with certain restrictions. That is, in some cases,sequences can be “warped” non-linearly to match each other. A thresholdmay be implemented such that the processor 104 would determine that thetwo video resumes are the same.

With continued reference to FIG. 1 , in some embodiments, processor 104may extract or otherwise recognize at least feature from video resume.Feature may be recognized and/or extracted from image component of videoresumes. In some cases, features may be recognized, which are associatedwith non-verbal content. For example, in some cases, visual non-verbalcontent such as expression of candidate's emotion may be represented bya number of features which are readily extracted from image component ofvideo resumes. In some cases, recognition and/or extraction of featuresfrom image component may include use of machine vision techniques.

Still referring to FIG. 1 , in some embodiments, apparatus 100 mayinclude a machine vision process. A machine vision process may use imagecomponent from video resume, to make a determination about verbal and/ornon-verbal content. For example, in some cases a machine vision processmay be used for world modeling or registration of objects within aspace. In some cases, registration and/or feature recognition mayinclude image processing, such as without limitation object recognition,feature detection, edge/corner detection, and the like. Non-limitingexample of feature detection 128 may include scale invariant featuretransform (SIFT), Canny edge detection, Shi Tomasi corner detection, andthe like. In some cases, a machine vision process may operate imageclassification and segmentation models, such as without limitation byway of machine vision resource (e.g., OpenMV or TensorFlow Lite). Amachine vision process may detect motion, for example by way of framedifferencing algorithms. A machine vision process may detect markers,for example blob detection, object detection, face detection, and thelike. In some cases, a machine vision process may perform eye tracking(i.e., gaze estimation). In some cases, a machine vision process mayperform person detection, for example by way of a trained machinelearning model. In some cases, a machine vision process may performmotion detection (e.g., camera motion and/or object motion), for exampleby way of optical flow detection. In some cases, machine vision processmay perform code (e.g., barcode) detection and decoding. In some cases,a machine vision process may additionally perform image capture and/orvideo recording.

Still referring to FIG. 1 , in some cases, machine vision process mayperform pose-estimation for example to ascertain a relative location ormovement of objects within existing video resumes to include one or moretransformations, for example to a view of a frame (or an image orexisting video resumes) relative a three-dimensional coordinate system;exemplary transformations include without limitation homographytransforms and affine transforms. In an embodiment, registration offirst frame to a coordinate system may be verified and/or correctedusing object identification and/or computer vision, as described above.For instance, and without limitation, an initial registration to twodimensions, represented for instance as registration to the x and ycoordinates, may be performed using a two-dimensional projection ofpoints in three dimensions onto a first frame, however. A thirddimension of registration, representing depth and/or a z axis, may bedetected by comparison of two frames; image recognition and/or edgedetection software may be used to detect multiple views of images of anobject (from subsequent frames) to derive a relative position along athird (z) axis. In some cases, solicitation video may include a stereoimage, having two stereoscopic views, which may be compared to derivez-axis values of points on object permitting, for instance, derivationof further z-axis points within and/or around the object usinginterpolation. Alternatively, or additionally, relative movement withinImage component 116 (e.g., frame to frame) may be used to ascertainpositions of objects, even along a z-axis, for instance by way ofkinetic parallax. In some cases, relative motion of objects further awaymay occur at a different speed than objects nearby, this phenomenon maybe used to ascertain a position of objects relative a camera, forexample when the camera is moving. Object recognition and poseestimation may be repeated with multiple objects in field of view,including without a subject. In an embodiment, x and y axes may bechosen to span a plane common to a field of view of a camera used forsolicitation video image capturing and/or an xy plane of a first frame;a result, x and y translational components and q may be pre-populated intranslational and rotational matrices, for affine transformation ofcoordinates of object, also as described above. Initial x and ycoordinates and/or guesses at transformational matrices mayalternatively or additionally be performed between first frame andsecond frame, as described above. For each point of a plurality ofpoints on object and/or edge and/or edges of object as described above,x and y coordinates of a first frame may be populated, with an initialestimate of z coordinates based, for instance, on assumptions aboutobject, such as an assumption that ground is substantially parallel toan xy plane as selected above. Z coordinates, and/or x, y, and zcoordinates, registered using image capturing and/or objectidentification processes as described above may then be compared tocoordinates predicted using initial guess at transformation matrices; anerror function may be computed using by comparing the two sets ofpoints, and new x, y, and/or z coordinates, may be iteratively estimatedand compared until the error function drops below a threshold level.

Still referring to FIG. 1 , in some cases, a machine vision process mayuse at least an image classifier, or any classifier described throughoutthis disclosure. As a non-limiting example, a machine vision process mayuse an image classifier, wherein the input is image component of videoresumes, and through a classification algorithm, outputs imagecomponents into categories based on training data, such as sequentialvideo resume frames that match a video resume from a different data set.Processor 104 and/or another device may generate a classifier using aclassification algorithm, defined as a process whereby a computingdevice derives a classifier from training data. Classification may beperformed using, without limitation, linear classifiers such as withoutlimitation logistic regression and/or I Bayes classifiers, nearestneighbor classifiers such as k-nearest neighbors classifiers, supportvector machines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 1 , comparing the assorter-linked data set 124with the endpoint-linked data set 128 may include recognizing writtentext. In an embodiment, data sets may include written resumes. Writtentext may go through a machine-learning process as discussed above.Written resume from the assorter-linked data set 124 may be compared tothe endpoint-linked data set 128 to identify similarities and determineif the two candidates are the same. Processor 104 may be configured torecognize at least a keyword as a function of visual verbal content. Insome cases, recognizing at least keyword may include optical characterrecognition. As used in this disclosure, a “keyword” is an element ofword or syntax used to identify and/or match elements to each other. Insome cases, processor 104 may transcribe much or even substantially allverbal content from a video resume.

Still referring to FIG. 1 , in some embodiments, optical characterrecognition or optical character reader (OCR) includes automaticconversion of images of written (e.g., typed, handwritten or printedtext) into machine-encoded text. In some cases, recognition of at leasta keyword 140 from an image component 116 a-b may include one or moreprocesses, including without limitation optical character recognition(OCR), optical word recognition, intelligent character recognition,intelligent word recognition, and the like. In some cases, OCR mayrecognize written text, one glyph or character at a time. In some cases,optical word recognition may recognize written text, one word at a time,for example, for languages that use a space as a word divider. In somecases, intelligent character recognition (ICR) may recognize writtentext one glyph or character at a time, for instance by employingmachine-learning processes. In some cases, intelligent word recognition(IWR) may recognize written text, one word at a time, for instance byemploying machine-learning processes.

Still referring to FIG. 1 , in some cases OCR may be an “offline”process, which analyses a static document or image frame. In some cases,handwriting movement analysis can be used as input to handwritingrecognition. For example, instead of merely using shapes of glyphs andwords, this technique may capture motions, such as the order in whichsegments are drawn, the direction, and the pattern of putting the pendown and lifting it. This additional information may make handwritingrecognition more accurate. In some cases, this technology may bereferred to as “online” character recognition, dynamic characterrecognition, real-time character recognition, and intelligent characterrecognition.

Still referring to FIG. 1 , in some cases, OCR processes may employpre-processing of image component. Pre-processing process may includewithout limitation de-skew, de-speckle, binarization, line removal,layout analysis or “zoning,” line and word detection, scriptrecognition, character isolation or “segmentation,” and normalization.In some cases, a de-skew process may include applying a transform (e.g.,homography or affine transform) to Image component to align text. Insome cases, a de-speckle process may include removing positive andnegative spots and/or smoothing edges. In some cases, a binarizationprocess may include converting an image from color or greyscale toblack-and-white (i.e., a binary image). Binarization may be performed asa simple way of separating text (or any other desired image component)from a background of image component. In some cases, binarization may berequired for example if an employed OCR algorithm only works on binaryimages. In some cases, a line removal process may include removal ofnon-glyph or non-character imagery (e.g., boxes and lines). In somecases, a layout analysis or “zoning” process may identify columns,paragraphs, captions, and the like as distinct blocks. In some cases, aline and word detection process may establish a baseline for word andcharacter shapes and separate words, if necessary. In some cases, ascript recognition process may, for example in multilingual documents,identify script allowing an appropriate OCR algorithm to be selected. Insome cases, a character isolation or “segmentation” process may separatesignal characters, for example character-based OCR algorithms. In somecases, a normalization process may normalize aspect ratio and/or scaleof image component.

Still referring to FIG. 1 , in some embodiments an OCR process mayinclude an OCR algorithm. Exemplary OCR algorithms include matrixmatching process and/or feature extraction processes. Matrix matchingmay involve comparing an image to a stored glyph on a pixel-by-pixelbasis. In some case, matrix matching may also be known as “patternmatching,” “pattern recognition,” and/or “image correlation.” Matrixmatching may rely on an input glyph being correctly isolated from therest of the image component. Matrix matching may also rely on a storedglyph being in a similar font and at a same scale as input glyph. Matrixmatching may work best with typewritten text.

Still referring to FIG. 1 , in some embodiments, an OCR process mayinclude a feature extraction process. In some cases, feature extractionmay decompose a glyph into at least a feature 128. Exemplarynon-limiting features may include corners, edges, lines, closed loops,line direction, line intersections, and the like. In some cases, featureextraction may reduce dimensionality of representation and may make therecognition process computationally more efficient. In some cases,extracted feature 128 may be compared with an abstract vector-likerepresentation of a character, which might reduce to one or more glyphprototypes. General techniques of feature detection in computer visionare applicable to this type of OCR. In some embodiments,machine-learning processes like nearest neighbor classifiers (e.g.,k-nearest neighbors algorithm) may be used to compare image featureswith stored glyph features and choose a nearest match. OCR may employany machine-learning process described in this disclosure, for examplemachine-learning processes described with reference to FIG. 6 .Exemplary non-limiting OCR software includes Cuneiform and Tesseract.Cuneiform is a multi-language, open-source optical character recognitionsystem originally developed by Cognitive Technologies of Moscow, Russia.Tesseract is free OCR software originally developed by Hewlett-Packardof Palo Alto, Calif., United States.

Still referring to FIG. 1 , in some cases, OCR may employ a two-passapproach to character recognition. A first pass may try to recognize acharacter. Each character that is satisfactory is passed to an adaptiveclassifier as training data. The adaptive classifier then gets a chanceto recognize characters more accurately as it further analyzes imagecomponents. Since the adaptive classifier may have learned somethinguseful a little too late to recognize characters on the first pass, asecond pass is run over the image components. Second pass may includeadaptive recognition and use characters recognized with high confidenceon the first pass to recognize better remaining characters on the secondpass. In some cases, two-pass approach may be advantageous for unusualfonts or low-quality image components where visual verbal content may bedistorted. Another exemplary OCR software tool include OCRopus. OCRopusdevelopment is led by German Research Centre for Artificial Intelligencein Kaiserslautern, Germany. In some cases, OCR software may employneural networks.

Still referring to FIG. 1 , in some cases, OCR may includepost-processing. For example, OCR accuracy may be increased, in somecases, if output is constrained by a lexicon. A lexicon may include alist or set of words that are allowed to occur in a document. In somecases, a lexicon may include, for instance, all the words in the Englishlanguage, or a more technical lexicon for a specific field. In somecases, an output stream may be a plain text stream or file ofcharacters. In some cases, an OCR process may preserve an originallayout of visual verbal content. In some cases, near-neighbor analysiscan make use of co-occurrence frequencies to correct errors, by notingthat certain words are often seen together. For example, “Washington,D.C.” is generally far more common in English than “Washington DOC.” Insome cases, an OCR process may make us of a priori knowledge of grammarfor a language being recognized. For example, grammar rules may be usedto help determine if a word is likely to be a verb or a noun. Distanceconceptualization may be employed for recognition and classification.For example, a Levenshtein distance algorithm may be used in OCRpost-processing to further optimize results.

With continued reference to FIG. 1 , processor 104 may be configured todetermine, as a function of the comparison result, a duplicationcoefficient for the video resumes. As used in this disclosure,“duplication coefficient” is a quantitative value of observedsimilarities between two or more data sets, including video resumes andwritten resumes. Duplication coefficient may be calculated or computedto provide a measure or metric of similarity between assorter-linkeddata set 124 and endpoint-linked data set 128. Duplication coefficientcould stand for “how much”, “how many” or “how often” data appears invideo resumes. The different categories of quantitative data couldinclude, measurements, counts, calculations, sensors, projections,quantification of qualitive data, and the like. Other examples,duplication coefficient could measure the number of candidates appearingper video resume. Duplication coefficient could quantify how manycandidates answered an interview question. Duplication coefficient couldcount candidates having similar technical backgrounds in each videoresume. Another example, duplication coefficient could calculate thefacial match between candidates in each video resume. In an embodiment,duplication coefficient may determine that video resumes are moresimilar if both candidates in the videos have similar technicalbackgrounds. In some embodiments, duplication coefficient may include ametric range on a scale of 0, where 0 means the video resumes are notalike, to 10, where 10 is the exact same video. It could also include arange of percentages and may cover any suitable range or rating score.In some cases, determining duplication coefficient data sets may includelinear regression analysis, machine-learning process, and the like. Forexample, duplication coefficient may be calculated by using classifierconfigured to output at least a datum that labels or otherwiseidentifies a set of data that are clustered together, found to be closeunder a distance metric. Additionally, or alternatively, duplicationcoefficient may be an output of a machine-learning module 120 thatgenerates classifier using a classification algorithm.

Still referring to FIG. 1 , processor 104 may utilize the duplicationcoefficient to output indications regarding data sets, such as informingsystem 100 that the two candidates are the same. Indications mayalternatively or additionally display a degree of similarity to anothervideo and/or video resume, may list one or more video resumes found tobe similar as a function of duplication coefficient, or the like. Insome cases, processor 104 may utilize the duplication coefficient toidentify whether the first and the second candidates are the samecandidate.

Additional information on visual and audio comparison is illustrated inU.S. patent application Ser. No. 17/582,070 entitled “APPARATUSES ANDMETHODS FOR PARSING AND COMPARING VIDEO RESUME DUPLICATIONS” filed onJan. 24, 2022.

Continuing to reference FIG. 1 , processor 104 of apparatus 100 isconfigured to process the transfer request 116. Transfer request 116 maybe processed after identifying the second candidate 148 as the firstcandidate 144. In an embodiment, apparatus 100 may ensure that theassorter 108 and the endpoint 112 are discussing an action datum 132 inregards to the same candidate. Processing the transfer request 116 mayinclude posting the action datum 132 of the transfer request 116 on animmutable sequential listing 200. This may ensure that the transferrequest 116 is unable to be altered such that all parties involved haverecords of the same action datum 132. Transfer request 116 may bedigitally signed by the transfer initiator. A digital signature may beused to verify the authenticity of the action datum 132. The transferinitiator may be the endpoint 112 or the assorter 108. Digitalsignatures and immutable sequential listings are discussed in furtherdetail below. Transfer request 116 includes authenticating the assorter108 and the endpoint 112. This may include authenticating the devicesthat they may use to send data sets and transfer requests. In anembodiment, authenticating the assorter 108 and the endpoint 112 mayensure that there are no malicious actors. As used herein, a “maliciousactor” is an entity that takes part in actions that cause harm to thecyber realm. The cyber realm, in this instance, refers to this apparatus100. In an embodiment, a malicious actor may be an entity (such as aperson) acting as the assorter 108 in order to gain access to thecompensation data and ultimately, the compensation. Compensation, suchas a monetary transaction, may also be posted on the immutablesequential listing.

With continued reference to FIG. 1 , authenticating may includeauthenticating a user identity. A user may be an assorter 108, endpoint112, candidate, or the like. Authenticating a user identity may includeauthenticating that a user is the owner of user device. Authenticatingmay include authenticating a user identity from an authentication datum140 provided by a user. An authentication datum 140 may be a knowledgefactor as a password that only user knows and only user is able to enterwhen prompted. Authenticating may include validating a user password,passphrase, and/or PIN. Authenticating may include authenticating apossession factor of a user such as authenticating a biometricauthentication of a user. Biometric authentication may include any ofthe biometric authentications described above, for example scanning auser fingerprint, scanning an iris, and/or measuring the gait of a user.Biometric authentication may ensure that a user device is being used bythe owner of user device 108. In an embodiment, biometric authenticationmay be unimodal whereby only one biometric authentication is performed,or biometric authentication may be multimodal whereby two or morebiometric authentications are performed. For example, a multimodalauthentication may include a fingerprint scan and an iris scan. In anembodiment, multimodal authentication may be simultaneous, whereby twoor more biometric authentications are occurring at the same time, ormultimodal authentication may be performed in succession, whereby onebiometric authentication is performed followed in succession by at leasta second biometric authentication.

With continued reference to FIG. 1 , authenticating may includecalculating a confidence level of a user device. Calculating aconfidence level may include calculating a confidence level as afunction of the at least a confidence level in authenticity of a userdevice. Confidence level in identity may be computed, for instance,using one or more statistical measures of reliability of theidentification method used; for instance, a user may enter aninstruction on a processor 104 providing statistics indicating successrates of various identification methods. Statistics may be collectedbased, as a non-limiting example, on discoveries of vulnerabilities inparticular identification protocols and/or particular instances ofsecure computation module. A user of system may alternatively make asubjective assessment, based on expert knowledge, for instance, of aconfidence level to assign based on such findings, and enter thatconfidence level. Statistics and/or user-entered confidence level inidentification method may be used as multipliers or otherwise combinedwith confidence-level calculations as described in further detail below,or otherwise calculating a confidence level as a function of theconfidence level in the identity. A device included in system 100 mayalso determine confidence level in identity as a function of, forinstance, one or more algorithms collecting statistics concerning degreeof accuracy in past iterations of a particular process for identifyingat least a distributed storage node.

With continued reference to FIG. 1 , at least a confidence level mayinclude evaluating a biometric authentication of a user and calculatinga confidence level to a user device as a function of the biometricauthentication of the user. Confidence level in biometric authenticationmay be computed, for instance, using one or more biometricauthentication measures to suggest if a user device is being used by itsowner. For instance, a variety of biometric authentication measures toconfirm behavior biometrics of a user may be tested, for example speech,voice, signature, keystroke, and/or gait may be measured and analyzed todetermine if a user device 108 is being used by its owner. Biometricauthentication measures may also employ the use of biometric sensors andscanners that may detect and acquire data necessary for biometricrecognition and verification. This may include for example, sensors thatmay scan and analyze a user face, palm, vein, fingerprint, iris, retina,hand geometry, finger geometry, tooth shape, radiographic dental image,ear shape, olfactory, speech, voice, signature, keystroke dynamicsrecorder, and/or devices to perform movement signature recognitionand/or gait energy images. Biometric sensors may incorporate other toolsand technologies such as optical imaging, ultrasonic imaging, andcapacitance imaging. In an embodiment, if a variety of biometricauthentication measures suggest a user device 108 is being used by itsowner, then biometric authentication accuracy may be reduced for a giventhreshold of transaction or time. For example, biometric authenticationof user based on typing behavior, location, and fingerprint recognitionmay authenticate user as owner of user device 108. This may allow userto complete an asset transfer within a set period of time without havingto reauthenticate user at a later stage in time. Alternatively oradditionally, biometric authentication of user based on typing behavior,location and fingerprint recognition may authenticate user as owner ofuser device 108 so that threshold confidence level that may be neededfor the remaining transaction may be reduced. For example, at subsequentstages in an asset transfer, user may only need to be authenticated byone biometric authentication, for example by a subsequent typingbehavior analysis. Fingerprint recognition may not be necessary forsubsequent authentications after being measured initially. In anembodiment, lowered confidence level for the remaining transaction mayexpire after a certain period of time in an attempt to prevent badactors from being able to infiltrate system 100 after an initialbiometric authentication has been performed.

Still referring to FIG. 1 , confidence level may be weighted or modifiedaccording to one or more additional factors. For instance, confidencelevel may be weighted according to how recently at least a user deviceand/or other device signed a digitally signed assertion in anauthenticated instance of immutable sequential listing 200, where a morerecently authenticated assertion may result in a higher confidence levelor higher weight assigned to the confidence level, and a less recentlyauthenticated assertion may result in a lower confidence level or alower weight assigned to that confidence level. As another example, adevice that has recently “sold off” a large amount of value and/or hasan assertion in a sub-listing currently awaiting authentication may haveits confidence level decreased. As a further example, an evaluator withlittle or no history, or an anonymous evaluator, may be assigned someminimal or “neutral” confidence level indicating treatment as a“consensus” evaluator rather than a “trusted” evaluator. An evaluatorassociated with a previous fraudulent transaction may be assigned aconfidence level of zero or may be excluded from evaluation processes.

Additional information on authorization of user identity is illustratedin U.S. patent application Ser. No. 16/861,699 entitled “SYSTEMS ANDMETHODS FOR CRYPTOGRAPHIC AUTHORIZATION OF WIRELESS COMMUNICATIONS”filed on Apr. 29, 2020.

Referring now to FIG. 2 , an exemplary embodiment of an immutablesequential listing 200 is illustrated. Data elements are listing inimmutable sequential listing 200; data elements may include any form ofdata, including textual data, image data, encrypted data,cryptographically hashed data, and the like. Data elements may include,without limitation, one or more at least a digitally signed assertions.In one embodiment, a digitally signed assertion 204 is a collection oftextual data signed using a secure proof as described in further detailbelow; secure proof may include, without limitation, a digital signatureas described above. Collection of textual data may contain any textualdata, including without limitation American Standard Code forInformation Interchange (ASCII), Unicode, or similar computer-encodedtextual data, any alphanumeric data, punctuation, diacritical mark, orany character or other marking used in any writing system to conveyinformation, in any form, including any plaintext or cyphertext data; inan embodiment, collection of textual data may be encrypted, or may be ahash of other data, such as a root or node of a Merkle tree or hashtree, or a hash of any other information desired to be recorded in somefashion using a digitally signed assertion 204. In an embodiment,collection of textual data states that the owner of a certaintransferable item represented in a digitally signed assertion 204register is transferring that item to the owner of an address. Adigitally signed assertion 204 may be signed by a digital signaturecreated using the private key associated with the owner's public key, asdescribed above.

Still referring to FIG. 2 , a digitally signed assertion 204 maydescribe a transfer of virtual currency, such as crypto-currency asdescribed below. The virtual currency may be a digital currency. Item ofvalue may be a transfer of trust, for instance represented by astatement vouching for the identity or trustworthiness of the firstentity. Item of value may be an interest in a fungible negotiablefinancial instrument representing ownership in a public or privatecorporation, a creditor relationship with a governmental body or acorporation, rights to ownership represented by an option, derivativefinancial instrument, commodity, debt-backed security such as a bond ordebenture or other security as described in further detail below. Aresource may be a physical machine e.g. a ride share vehicle or anyother asset. A digitally signed assertion 204 may describe the transferof a physical good; for instance, a digitally signed assertion 204 maydescribe the sale of a product. In some embodiments, a transfernominally of one item may be used to represent a transfer of anotheritem; for instance, a transfer of virtual currency may be interpreted asrepresenting a transfer of an access right; conversely, where the itemnominally transferred is something other than virtual currency, thetransfer itself may still be treated as a transfer of virtual currency,having value that depends on many potential factors including the valueof the item nominally transferred and the monetary value attendant tohaving the output of the transfer moved into a particular user'scontrol. The item of value may be associated with a digitally signedassertion 204 by means of an exterior protocol, such as the COLOREDCOINS created according to protocols developed by The Colored CoinsFoundation, the MASTERCOIN protocol developed by the MastercoinFoundation, or the ETHEREUM platform offered by the Stiftung EthereumFoundation of Baar, Switzerland, the Thunder protocol developed byThunder Consensus, or any other protocol.

Still referring to FIG. 2 , in one embodiment, an address is a textualdatum identifying the recipient of virtual currency or another item ofvalue in a digitally signed assertion 204. In some embodiments, addressis linked to a public key, the corresponding private key of which isowned by the recipient of a digitally signed assertion 204. Forinstance, address may be the public key. Address may be arepresentation, such as a hash, of the public key. Address may be linkedto the public key in memory of a processor 104, for instance via a“wallet shortener” protocol. Where address is linked to a public key, atransferee in a digitally signed assertion 204 may record a subsequent adigitally signed assertion 204 transferring some or all of the valuetransferred in the first a digitally signed assertion 204 to a newaddress in the same manner. A digitally signed assertion 204 may containtextual information that is not a transfer of some item of value inaddition to, or as an alternative to, such a transfer. For instance, asdescribed in further detail below, a digitally signed assertion 204 mayindicate a confidence level associated with a distributed storage nodeas described in further detail below.

In an embodiment, and still referring to FIG. 2 immutable sequentiallisting 200 records a series of at least a posted content in a way thatpreserves the order in which the at least a posted content took place.Temporally sequential listing may be accessible at any of varioussecurity settings; for instance, and without limitation, temporallysequential listing may be readable and modifiable publicly, may bepublicly readable but writable only by entities and/or devices havingaccess privileges established by password protection, confidence level,or any device authentication procedure or facilities described herein,or may be readable and/or writable only by entities and/or deviceshaving such access privileges. Access privileges may exist in more thanone level, including, without limitation, a first access level orcommunity of permitted entities and/or devices having ability to read,and a second access level or community of permitted entities and/ordevices having ability to write; first and second community may beoverlapping or non-overlapping. In an embodiment, posted content and/orimmutable sequential listing 200 may be stored as one or more zeroknowledge sets (ZKS), Private Information Retrieval (PIR) structure, orany other structure that allows checking of membership in a set byquerying with specific properties. Such database may incorporateprotective measures to ensure that malicious actors may not query thedatabase repeatedly in an effort to narrow the members of a set toreveal uniquely identifying information of a given posted content.

Still referring to FIG. 2 , immutable sequential listing 200 maypreserve the order in which the at least a posted content took place bylisting them in chronological order; alternatively or additionally,immutable sequential listing 200 may organize digitally signedassertions 204 into sub-listings 208 such as “blocks” in a blockchain,which may be themselves collected in a temporally sequential order;digitally signed assertions 204 within a sub-listing 208 may or may notbe temporally sequential. The ledger may preserve the order in which atleast a posted content took place by listing them in sub-listings 208and placing the sub-listings 208 in chronological order. The immutablesequential listing 200 may be a distributed, consensus-based ledger,such as those operated according to the protocols promulgated by RippleLabs, Inc., of San Francisco, Calif., or the Stellar DevelopmentFoundation, of San Francisco, Calif., or of Thunder Consensus. In someembodiments, the ledger is a secured ledger; in one embodiment, asecured ledger is a ledger having safeguards against alteration byunauthorized parties. The ledger may be maintained by a proprietor, suchas a system administrator on a server, that controls access to theledger; for instance, the user account controls may allow contributorsto the ledger to add at least a posted content to the ledger, but maynot allow any users to alter at least a posted content that have beenadded to the ledger. In some embodiments, ledger is cryptographicallysecured; in one embodiment, a ledger is cryptographically secured whereeach link in the chain contains encrypted or hashed information thatmakes it practically infeasible to alter the ledger without betrayingthat alteration has taken place, for instance by requiring that anadministrator or other party sign new additions to the chain with adigital signature. Immutable sequential listing 200 may be incorporatedin, stored in, or incorporate, any suitable data structure, includingwithout limitation any database, datastore, file structure, distributedhash table, directed acyclic graph or the like. In some embodiments, thetimestamp of an entry is cryptographically secured and validated viatrusted time, either directly on the chain or indirectly by utilizing aseparate chain. In one embodiment the validity of timestamp is providedusing a time stamping authority as described in the RFC 3161 standardfor trusted timestamps, or in the ANSI ASC x9.95 standard. In anotherembodiment, the trusted time ordering is provided by a group of entitiescollectively acting as the time stamping authority with a requirementthat a threshold number of the group of authorities sign the timestamp.

In some embodiments, and with continued reference to FIG. 2 , immutablesequential listing 200, once formed, may be inalterable by any party, nomatter what access rights that party possesses. For instance, immutablesequential listing 200 may include a hash chain, in which data is addedduring a successive hashing process to ensure non-repudiation. Immutablesequential listing 200 may include a block chain. In one embodiment, ablock chain is immutable sequential listing 200 that records one or morenew at least a posted content in a data item known as a sub-listing 208or “block.” An example of a block chain is the BITCOIN block chain usedto record BITCOIN transactions and values. Sub-listings 208 may becreated in a way that places the sub-listings 208 in chronological orderand link each sub-listing 208 to a previous sub-listing 208 in thechronological order so that any processor 104 may traverse thesub-listings 208 in reverse chronological order to verify any at least aposted content listed in the block chain. Each new sub-listing 208 maybe required to contain a cryptographic hash describing the previoussub-listing 208. In some embodiments, the block chain contains a singlefirst sub-listing 208 sometimes known as a “genesis block.”

Still referring to FIG. 2 , the creation of a new sub-listing 208 may becomputationally expensive; for instance, the creation of a newsub-listing 208 may be designed by a “proof of work” protocol acceptedby all participants in forming the immutable sequential listing 200 totake a powerful set of computing devices a certain period of time toproduce. Where one sub-listing 208 takes less time for a given set ofcomputing devices to produce the sub-listing 208 protocol may adjust thealgorithm to produce the next sub-listing 208 so that it will requiremore steps; where one sub-listing 208 takes more time for a given set ofcomputing devices to produce the sub-listing 208 protocol may adjust thealgorithm to produce the next sub-listing 208 so that it will requirefewer steps. As an example, protocol may require a new sub-listing 208to contain a cryptographic hash describing its contents; thecryptographic hash may be required to satisfy a mathematical condition,achieved by having the sub-listing 208 contain a number, called a nonce,whose value is determined after the fact by the discovery of the hashthat satisfies the mathematical condition. Continuing the example, theprotocol may be able to adjust the mathematical condition so that thediscovery of the hash describing a sub-listing 208 and satisfying themathematical condition requires more or less steps, depending on theoutcome of the previous hashing attempt. Mathematical condition, as anexample, might be that the hash contains a certain number of leadingzeros and a hashing algorithm that requires more steps to find a hashcontaining a greater number of leading zeros, and fewer steps to find ahash containing a lesser number of leading zeros. In some embodiments,production of a new sub-listing 208 according to the protocol is knownas “mining.” The creation of a new sub-listing 208 may be designed by a“proof of stake” protocol as will be apparent to those skilled in theart upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 2 , in some embodiments, protocol alsocreates an incentive to mine new sub-listings 208. The incentive may befinancial; for instance, successfully mining a new sub-listing 208 mayresult in the person or entity that mines the sub-listing 208 receivinga predetermined amount of currency. The currency may be fiat currency.Currency may be cryptocurrency as defined below. In other embodiments,incentive may be redeemed for particular products or services; theincentive may be a gift certificate with a particular business, forinstance. In some embodiments, incentive is sufficiently attractive tocause participants to compete for the incentive by trying to race eachother to the creation of sub-listings 208 Each sub-listing 208 createdin immutable sequential listing 200 may contain a record or at least aposted content describing one or more addresses that receive anincentive, such as virtual currency, as the result of successfullymining the sub-listing 208.

With continued reference to FIG. 2 , where two entities simultaneouslycreate new sub-listings 208, immutable sequential listing 200 maydevelop a fork; protocol may determine which of the two alternatebranches in the fork is the valid new portion of the immutablesequential listing 200 by evaluating, after a certain amount of time haspassed, which branch is longer. “Length” may be measured according tothe number of sub-listings 208 in the branch. Length may be measuredaccording to the total computational cost of producing the branch.Protocol may treat only at least a posted content contained the validbranch as valid at least a posted content. When a branch is foundinvalid according to this protocol, at least a posted content registeredin that branch may be recreated in a new sub-listing 208 in the validbranch; the protocol may reject “double spending” at least a postedcontent that transfer the same virtual currency that another at least aposted content in the valid branch has already transferred. As a result,in some embodiments the creation of fraudulent at least a posted contentrequires the creation of a longer immutable sequential listing 200branch by the entity attempting the fraudulent at least a posted contentthan the branch being produced by the rest of the participants; as longas the entity creating the fraudulent at least a posted content islikely the only one with the incentive to create the branch containingthe fraudulent at least a posted content, the computational cost of thecreation of that branch may be practically infeasible, guaranteeing thevalidity of all at least a posted content in the immutable sequentiallisting 200.

Still referring to FIG. 2 , additional data linked to at least a postedcontent may be incorporated in sub-listings 208 in the immutablesequential listing 200; for instance, data may be incorporated in one ormore fields recognized by block chain protocols that permit a person orcomputer forming a at least a posted content to insert additional datain the immutable sequential listing 200. In some embodiments, additionaldata is incorporated in an unspendable at least a posted content field.For instance, the data may be incorporated in an OP RETURN within theBITCOIN block chain. In other embodiments, additional data isincorporated in one signature of a multi-signature at least a postedcontent. In an embodiment, a multi-signature at least a posted contentis at least a posted content to two or more addresses. In someembodiments, the two or more addresses are hashed together to form asingle address, which is signed in the digital signature of the at leasta posted content. In other embodiments, the two or more addresses areconcatenated. In some embodiments, two or more addresses may be combinedby a more complicated process, such as the creation of a Merkle tree orthe like. In some embodiments, one or more addresses incorporated in themulti-signature at least a posted content are typical crypto-currencyaddresses, such as addresses linked to public keys as described above,while one or more additional addresses in the multi-signature at least aposted content contain additional data related to the at least a postedcontent; for instance, the additional data may indicate the purpose ofthe at least a posted content, aside from an exchange of virtualcurrency, such as the item for which the virtual currency was exchanged.In some embodiments, additional information may include networkstatistics for a given node of network, such as a distributed storagenode, e.g. the latencies to nearest neighbors in a network graph, theidentities or identifying information of neighboring nodes in thenetwork graph, the trust level and/or mechanisms of trust (e.g.certificates of physical encryption keys, certificates of softwareencryption keys, (in non-limiting example certificates of softwareencryption may indicate the firmware version, manufacturer, hardwareversion and the like), certificates from a trusted third party,certificates from a decentralized anonymous authentication procedure,and other information quantifying the trusted status of the distributedstorage node) of neighboring nodes in the network graph, IP addresses,GPS coordinates, and other information informing location of the nodeand/or neighboring nodes, geographically and/or within the networkgraph. In some embodiments, additional information may include historyand/or statistics of neighboring nodes with which the node hasinteracted. In some embodiments, this additional information may beencoded directly, via a hash, hash tree or other encoding.

With continued reference to FIG. 2 , in some embodiments, virtualcurrency is traded as a crypto-currency. In one embodiment, acrypto-currency is a digital, currency such as Bitcoins, Peercoins,Namecoins, and Litecoins. Crypto-currency may be a clone of anothercrypto-currency. The crypto-currency may be an “alt-coin.”Crypto-currency may be decentralized, with no particular entitycontrolling it; the integrity of the crypto-currency may be maintainedby adherence by its participants to established protocols for exchangeand for production of new currency, which may be enforced by softwareimplementing the crypto-currency. Crypto-currency may be centralized,with its protocols enforced or hosted by a particular entity. Forinstance, crypto-currency may be maintained in a centralized ledger, asin the case of the XRP currency of Ripple Labs, Inc., of San Francisco,Calif. In lieu of a centrally controlling authority, such as a nationalbank, to manage currency values, the number of units of a particularcrypto-currency may be limited; the rate at which units ofcrypto-currency enter the market may be managed by a mutuallyagreed-upon process, such as creating new units of currency whenmathematical puzzles are solved, the degree of difficulty of the puzzlesbeing adjustable to control the rate at which new units enter themarket. Mathematical puzzles may be the same as the algorithms used tomake productions of sub-listings 208 in a block chain computationallychallenging; the incentive for producing sub-listings 208 may includethe grant of new crypto-currency to the miners. Quantities ofcrypto-currency may be exchanged using at least a posted content asdescribed above.

Turning now to FIG. 3 , an exemplary embodiment of a cryptographicaccumulator 300 is illustrated. Cryptographic accumulator 300 has aplurality of accumulated elements 304, each accumulated element 304generated from a lot of the plurality of data lots. Accumulated elements304 are create using an encryption process, defined for this purpose asa process that renders the lots of data unintelligible from theaccumulated elements 304; this may be a one-way process such as acryptographic hashing process and/or a reversible process such asencryption. Cryptographic accumulator 300 further includes structuresand/or processes for conversion of accumulated elements 304 to root 312element. For instance, and as illustrated for exemplary purposes in FIG.3 , cryptographic accumulator 300 may be implemented as a Merkle treeand/or hash tree, in which each accumulated element 304 created bycryptographically hashing a lot of data. Two or more accumulatedelements 304 may be hashed together in a further cryptographic hashingprocess to produce a node 308 element; a plurality of node 308 elementsmay be hashed together to form parent nodes 308, and ultimately a set ofnodes 308 may be combined and cryptographically hashed to form root 312.Contents of root 312 may thus be determined by contents of nodes 308used to generate root 312, and consequently by contents of accumulatedelements 304, which are determined by contents of lots used to generateaccumulated elements 304. As a result of collision resistance andavalanche effects of hashing algorithms, any change in any lot,accumulated element 304, and/or node 308 is virtually certain to cause achange in root 312; thus, it may be computationally infeasible to modifyany element of Merkle and/or hash tree without the modification beingdetectable as generating a different root 312. In an embodiment, anyaccumulated element 304 and/or all intervening nodes 308 betweenaccumulated element 304 and root 312 may be made available withoutrevealing anything about a lot of data used to generate accumulatedelement 304; lot of data may be kept secret and/or demonstrated with asecure proof as described below, preventing any unauthorized party fromacquiring data in lot.

Continuing to refer to FIG. 3 , a “secure proof,” as used in thisdisclosure, is a protocol whereby an output is generated thatdemonstrates possession of a secret, such as device-specific secret,without demonstrating the entirety of the device-specific secret; inother words, a secure proof by itself, is insufficient to reconstructthe entire device-specific secret, enabling the production of at leastanother secure proof using at least a device-specific secret. A secureproof may be referred to as a “proof of possession” or “proof ofknowledge” of a secret. Where at least a device-specific secret is aplurality of secrets, such as a plurality of challenge-response pairs, asecure proof may include an output that reveals the entirety of one ofthe plurality of secrets, but not all of the plurality of secrets; forinstance, secure proof may be a response contained in onechallenge-response pair. In an embodiment, proof may not be secure; inother words, proof may include a one-time revelation of at least adevice-specific secret, for instance as used in a singlechallenge-response exchange.

Secure proof may include a zero-knowledge proof, which may provide anoutput demonstrating possession of a secret while revealing none of thesecret to a recipient of the output; zero-knowledge proof may beinformation-theoretically secure, meaning that an entity with infinitecomputing power would be unable to determine secret from output.Alternatively, zero-knowledge proof may be computationally secure,meaning that determination of secret from output is computationallyinfeasible, for instance to the same extent that determination of aprivate key from a public key in a public key cryptographic system iscomputationally infeasible. Zero-knowledge proof algorithms maygenerally include a set of two algorithms, a prover algorithm, or “P,”which is used to prove computational integrity and/or possession of asecret, and a verifier algorithm, or “V” whereby a party may check thevalidity of P. Zero-knowledge proof may include an interactivezero-knowledge proof, wherein a party verifying the proof must directlyinteract with the proving party; for instance, the verifying and provingparties may be required to be online, or connected to the same networkas each other, at the same time. Interactive zero-knowledge proof mayinclude a “proof of knowledge” proof, such as a Schnorr algorithm forproof on knowledge of a discrete logarithm. in a Schnorr algorithm, aprover commits to a randomness r, generates a message based on r, andgenerates a message adding r to a challenge c multiplied by a discretelogarithm that the prover is able to calculate; verification isperformed by the verifier who produced c by exponentiation, thuschecking the validity of the discrete logarithm. Interactivezero-knowledge proofs may alternatively or additionally include sigmaprotocols. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various alternative interactivezero-knowledge proofs that may be implemented consistently with thisdisclosure.

Alternatively, zero-knowledge proof may include a non-interactivezero-knowledge, proof, or a proof wherein neither party to the proofinteracts with the other party to the proof; for instance, each of aparty receiving the proof and a party providing the proof may receive areference datum which the party providing the proof may modify orotherwise use to perform the proof. As a non-limiting example,zero-knowledge proof may include a succinct non-interactive arguments ofknowledge (ZK-SNARKS) proof, wherein a “trusted setup” process createsproof and verification keys using secret (and subsequently discarded)information encoded using a public key cryptographic system, a proverruns a proving algorithm using the proving key and secret informationavailable to the prover, and a verifier checks the proof using theverification key; public key cryptographic system may include RSA,elliptic curve cryptography, ElGamal, or any other suitable public keycryptographic system. Generation of trusted setup may be performed usinga secure multiparty computation so that no one party has control of thetotality of the secret information used in the trusted setup; as aresult, if any one party generating the trusted setup is trustworthy,the secret information may be unrecoverable by malicious parties. Asanother non-limiting example, non-interactive zero-knowledge proof mayinclude a Succinct Transparent Arguments of Knowledge (ZK-STARKS)zero-knowledge proof. In an embodiment, a ZK-STARKS proof includes aMerkle root of a Merkle tree representing evaluation of a secretcomputation at some number of points, which may be 1 billion points,plus Merkle branches representing evaluations at a set of randomlyselected points of the number of points; verification may includedetermining that Merkle branches provided match the Merkle root, andthat point verifications at those branches represent valid values, wherevalidity is shown by demonstrating that all values belong to the samepolynomial created by transforming the secret computation. In anembodiment, ZK-STARKS does not require a trusted setup.

Zero-knowledge proof may include any other suitable zero-knowledgeproof. Zero-knowledge proof may include, without limitationbulletproofs. Zero-knowledge proof may include a homomorphic public-keycryptography (hPKC)-based proof. Zero-knowledge proof may include adiscrete logarithmic problem (DLP) proof. Zero-knowledge proof mayinclude a secure multi-party computation (MPC) proof. Zero-knowledgeproof may include, without limitation, an incrementally verifiablecomputation (IVC). Zero-knowledge proof may include an interactiveoracle proof (IOP). Zero-knowledge proof may include a proof based onthe probabilistically checkable proof (PCP) theorem, including a linearPCP (LPCP) proof. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms ofzero-knowledge proofs that may be used, singly or in combination,consistently with this disclosure.

In an embodiment, secure proof is implemented using a challenge-responseprotocol. In an embodiment, this may function as a one-time padimplementation; for instance, a manufacturer or other trusted party mayrecord a series of outputs (“responses”) produced by a device possessingsecret information, given a series of corresponding inputs(“challenges”), and store them securely. In an embodiment, achallenge-response protocol may be combined with key generation. Asingle key may be used in one or more digital signatures as described infurther detail below, such as signatures used to receive and/or transferpossession of crypto-currency assets; the key may be discarded forfuture use after a set period of time. In an embodiment, varied inputsinclude variations in local physical parameters, such as fluctuations inlocal electromagnetic fields, radiation, temperature, and the like, suchthat an almost limitless variety of private keys may be so generated.Secure proof may include encryption of a challenge to produce theresponse, indicating possession of a secret key. Encryption may beperformed using a private key of a public key cryptographic system, orusing a private key of a symmetric cryptographic system; for instance,trusted party may verify response by decrypting an encryption ofchallenge or of another datum using either a symmetric or public-keycryptographic system, verifying that a stored key matches the key usedfor encryption as a function of at least a device-specific secret. Keysmay be generated by random variation in selection of prime numbers, forinstance for the purposes of a cryptographic system such as RSA thatrelies prime factoring difficulty. Keys may be generated by randomizedselection of parameters for a seed in a cryptographic system, such aselliptic curve cryptography, which is generated from a seed. Keys may beused to generate exponents for a cryptographic system such asDiffie-Helman or ElGamal that are based on the discrete logarithmproblem.

Alternatively or additionally, and still referring to FIG. 3 ,cryptographic accumulator 300 may include a “vector commitment” whichmay act as an accumulator in which an order of elements in set ispreserved in its root 312 and/or commitment. In an embodiment, a vectorcommitment may be a position binding commitment and can be opened at anyposition to a unique value with a short proof (sublinear in the lengthof the vector). A Merkle tree may be seen as a vector commitment withlogarithmic size openings. Subvector commitments may include vectorcommitments where a subset of the vector positions can be opened in asingle short proof (sublinear in the size of the subset). Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various alternative or additional cryptographic accumulators300 that may be used as described herein. In addition to Merkle trees,accumulators may include without limitation RSA accumulators, classgroup accumulators, and/or bi-linear pairing-based accumulators. Anyaccumulator may operate using one-way functions that are easy to verifybut infeasible to reverse, i.e. given an input it is easy to produce anoutput of the one-way function, but given an output it iscomputationally infeasible and/or impossible to generate the input thatproduces the output via the one-way function. For instance, and by wayof illustration, a Merkle tree may be based on a hash function asdescribed above. Data elements may be hashed and grouped together. Then,the hashes of those groups may be hashed again and grouped together withthe hashes of oilier groups; this hashing and grouping may continueuntil only a single hash remains. As a further non-limiting example, RSAand cuss group accumulators may be based on the fact that it isinfeasible to compute an arbitrary root of an element in a cyclic groupof unknown order, whereas arbitrary powers of elements are easy tocompute. A data element may be added to the accumulator by hashing thedata element successively until the hash is a prime number and thentaking the accumulator to the power of that prime number. The witnessmay be the accumulator prior to exponentiation. Bi-linear paring-basedaccumulators may be based on the infeasibility found in elliptic curvecryptography, namely that finding a number k such that adding to itselfk times results in Q is impractical, whereas confirming that, given 4points P, Q, R, S, the point, P needs to be added as many times toitself to result in Q as R needs to be added as many times to itself toresult in S, can be computed efficiently for certain elliptic curves.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 120 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module 120may perform determinations, classification, and/or analysis steps,methods, processes, or the like as described in this disclosure usingmachine learning processes. A “machine learning process,” as used inthis disclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a processor 104/moduleto produce outputs 408 given data provided as inputs 412; this is incontrast to a non-machine learning software program where the commandsto be executed are determined in advance by a user and written in aprogramming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 120 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeinputs may include subject-specific data and outputs may includedescription-specific data.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 120 may generate aclassifier using a classification algorithm, defined as a processeswhereby a processor 104 and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data to accordingto fields of job description for instance, title, role, organization,requisite experience, requisite credentials, and the like.

Still referring to FIG. 4 , machine-learning module 120 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude subject-specific data as described above as inputs,description-specific data as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 404. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process428 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 120 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 5 , an exemplary embodiment of neural network 500is illustrated. A neural network 500 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.”

Referring now to FIG. 6 , an exemplary embodiment of a node of a neuralnetwork is illustrated. A node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function co, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above.

Referring now to FIG. 7 , an exemplary embodiment of method 700 ofcandidate tracking is shown. Step 705 of method 700 includes receivingassorter-linked data set 124 associated with a first candidate 144. Theassorter-linked data set 124 may include written or video resumes, etc.The data set may include candidate information such ascandidate-associated data. This may be implemented, without limitation,as described above in reference to FIG. 1-6 . Step 710 includesreceiving endpoint-linked data set 128 associated with a secondcandidate 148. Endpoint-linked data set 128 may include similarinformation as the assorter-linked data set 124. This may beimplemented, without limitation, as described above in reference to FIG.1-6 .

Step 715 of method 700 includes receiving a transfer request 116,wherein the transfer request 116 includes an action datum 132. Actiondatum 132 may include an action requested from the assorter 108 to theendpoint 112, or vise versa. In an embodiment, action datum 132 mayinclude an action such as compensation of an assorter 108. This may beimplemented, without limitation, as described above in reference to FIG.1-6 . Step 720 includes determining a degree of match 136 between theassorter-linked data set 124 and the endpoint-linked data set 128.Degree of match 136 may include a comparison and comparison scorebetween the two data sets. In an embodiment, this may include comparingvideo resumes of the two data sets to identify a job application. Thismay be implemented, without limitation, as described above in referenceto FIG. 1-6 .

Step 725 of method 700 includes identifying the second candidate 148 asthe first candidate 144. In an embodiment, processor may utilizecomparison result of the two data sets to determine that the twocandidates are the same. This may be implemented, without limitation, asdescribed above in reference to FIG. 1-6 . Step 730 of method 700includes processing the transfer request 116, wherein processing thetransfer request 116 includes authenticating the assorter 108 and theendpoint 112. This may be implemented, without limitation, as describedabove in reference to FIG. 1-6 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 882 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 882 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 886. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. An apparatus for candidate tracking using classifiers, the apparatuscomprising: at least a processor; and a memory communicatively connectedto the at least a processor and including instructions configuring theat least a processor to: receive an assorter-linked data set associatedwith a first candidate, wherein the assorter-linked data set comprises afirst video resume; extract at least a feature from the first videoresume through a machine vision process; receive an endpoint-linked dataset associated with a second candidate, wherein the endpoint-linked dataset comprises a second video resume; extract at least a feature from thesecond video resume through a machine vision process; receive a transferrequest, wherein the transfer request comprises an action datum, whereinthe action datum comprises compensation data that includes a transfer ofpayment between an endpoint and an assorter; determine a degree of matchbetween the assorter-linked data set and the endpoint-linked data set,wherein the degree of match comprises a similarity ranking between theat least a feature from the first video resume and the at least afeature from the second video resume; determine the first candidate andthe second candidate include a same candidate as a function of thesimilarity ranking; storing the determination of the same candidate inan immutable sequential listing, wherein the immutable sequence listingcomprises blocks containing entries of data further comprising thedetermination of the same candidate; and verifying the action datum ofthe transfer request as a function of the immutable sequential listing.2. The apparatus of claim 1, wherein the machine vision process includesa pose estimation process.
 3. The apparatus of claim 1, wherein the datasets are posted on the immutable sequential listing.
 4. The apparatus ofclaim 3, wherein posting the data sets on the immutable sequentiallisting further comprises storing recruitment process progression of thefirst and second candidate in a plurality of data blocks of theimmutable sequential listing.
 5. The apparatus of claim 1, furthercomprising authenticating the transfer request, wherein authenticatingthe transfer request comprises authenticating endpoint identity bybiometric authentication.
 6. The apparatus of claim 5, whereinauthenticating the transfer request further comprises authenticatingplacement identity by biometric authentication.
 7. The apparatus ofclaim 1, wherein determining the similarity ranking further comprisesperforming dynamic time warping of the first video resume and the secondvideo resume.
 8. (canceled)
 9. The apparatus of claim 1, furthercomprising processing the transfer request based on the verification ofthe action datum, wherein processing the transfer request comprisesdigitally signing the transfer request.
 10. The apparatus of claim 1,wherein verifying the transfer request further comprises entering theaction datum on an immutable sequential listing.
 11. A method forcandidate tracking, the method comprising: receiving an assorter-linkeddata set associated with a first candidate, wherein the assorter-linkeddata set comprises a first video resume; extracting at least a featurefrom the first video resume through a machine vision process; receivingan endpoint-linked data set associated with a second candidate, whereinthe endpoint-linked data set comprises a second vide resume; extractingat least a feature from the second video resume through a machine visionprocess; receiving a transfer request, wherein the transfer requestcomprises an action datum, wherein the action datum comprisescompensation data that includes a transfer of payment between anendpoint and an assorter; determining a degree of match between theplacement-linked data set and the endpoint-linked data set, wherein thedegree of match comprises a similarity ranking between the at least afeature from the first video resume and the second video resume;determining the first candidate and the second candidate include a samecandidate as a function of the similarity ranking; and storing thedetermination of the same candidate in an immutable sequential listing,wherein the immutable sequence listing comprises blocks containingentries of data further comprising the determination of the samecandidate; and verifying the action datum of the transfer request as afunction of the immutable sequential listing.
 12. The method of claim11, wherein the machine vision process includes a pose estimationprocess.
 13. The method of claim 11, wherein the data sets are posted onthe immutable sequential listing.
 14. The method of claim 13, whereinposting the data sets on the immutable sequential listing furthercomprises storing recruitment process progression of the first andsecond candidate in a plurality of data blocks of the immutablesequential listing.
 15. The method of claim 11, further comprisingauthenticating the transfer request, wherein authenticating the transferrequest comprises authenticating endpoint identity by biometricauthentication.
 16. The method of claim 15, wherein authenticating thetransfer request further comprises authenticating placement identity bybiometric authentication.
 17. The method of claim 11, whereindetermining the similarity ranking further comprise performing dynamictime warping of the first video resume and the second video resume. 18.(canceled)
 19. The method of claim 11, further comprising processing thetransfer request based on the verification of the action datum, whereinprocessing the transfer request comprises digitally signing the transferrequest.
 20. The method of claim 11, wherein verifying the transferrequest further comprises entering the action datum on an immutablesequential listing.
 21. The apparatus of claim 1, wherein thecompensation data is generated using a machine-learning model as afunction of an education level of the first candidate.
 22. The apparatusof claim 5, wherein the transfer request comprises authenticating adevice of the assorter, wherein authenticating the device comprisescalculating a confidence level as a function of the biometricauthentication.